# Libraries used
#Manipulation/Calucaltion
import pandas as pd
import pyreadstat 
import numpy as np
from statsmodels.miscmodels.ordinal_model import OrderedModel
#plotting
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.io as pio
pio.renderers.default = "notebook"

#goefileloade/read
import geopandas as gpd
import json
# pandast option setup
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 15)
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

# Loading the main dataset: df 
df, meta = pyreadstat.read_sav('Project_Dataset.sav')

df

# setuping the variable view
variable_view = pd.DataFrame({
    "Variable Name": meta.column_names,
    "Label": meta.column_labels,
    "Values": [meta.variable_value_labels.get(var, {}) for var in meta.column_names]
})

variable_view.head(10)

# seting up the labeled view dataframe: labeled_df
labeled_df = df.copy()
for column, value_labels in meta.variable_value_labels.items():
    if column in labeled_df.columns:
        # Ensure consistent types between DataFrame values and value_labels keys
        value_labels = {float(k): v for k, v in value_labels.items()}  # Convert keys to float
        labeled_df[column] = labeled_df[column].map(value_labels).fillna(labeled_df[column])

labeled_df.head(5)

# searching for anything that contains the letters that are in Satisfaction
variable_view[variable_view['Label'].str.contains('Sati')]

# searching for anything that contains the letters that are in Happiness 
variable_view[variable_view['Label'].str.contains('hap')]

# combining the results that we want
DataFrame = pd.DataFrame({
    'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
    'Feeling of happiness (NV_Q46)': df['Q46'],
    'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
})

DataFrame

# setting up the frequency table for Feeling of happiness
FoH_frequency_df = DataFrame[['Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)']].value_counts().reset_index().sort_values(by='Feeling of happiness (NV_Q46)')
# calculating the percentages of Feeling of happiness
perFoH = DataFrame[['Feeling of happiness (LV_Q46)']].value_counts(normalize=True)*100
# merge Percentage of Feeling of happiness
FoH_frequency_df = FoH_frequency_df.merge(perFoH, on='Feeling of happiness (LV_Q46)')
#rename the proportion to perFoH
FoH_frequency_df.rename(columns={'proportion':'perFoH'}, inplace=True)
#calculating the mean
mean_SFSH=DataFrame.groupby('Feeling of happiness (LV_Q46)')['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index().sort_values(by= 'Feeling of happiness (LV_Q46)', ascending=False)
# mearging the mean of Satisfaction with financial situation of household with Feeling of happiness frequency table
FoH_frequency_df=FoH_frequency_df.merge(mean_SFSH, how='left', on='Feeling of happiness (LV_Q46)')

FoH_frequency_df.rename(columns={'Satisfaction with financial situation of household (NV_Q50)':'mean_SFSH'}, inplace=True)

FoH_frequency_df

# calculate the mode and median
Mode_FOH = DataFrame['Feeling of happiness (NV_Q46)'].mode()[0] # Get the first mode
Median_FOH = DataFrame['Feeling of happiness (NV_Q46)'].median()

# assign the mode and median to the columns (handling missing values)
FoH_frequency_df['Mode_FOH'] = Mode_FOH
FoH_frequency_df['Median_FOH'] = Median_FOH

# fill any NaN values if needed
FoH_frequency_df['Mode_FOH'].fillna(Mode_FOH, inplace=True)
FoH_frequency_df['Median_FOH'].fillna(Median_FOH, inplace=True)


order = ['Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','perFoH','Median_FOH','Mode_FOH','count','mean_SFSH']

FoH_frequency_df[order]

Mode_FEC = (
    DataFrame.groupby('Feeling of happiness (LV_Q46)')['Feeling of happiness (NV_Q46)']
    .agg(lambda x: x.mode())
    .reset_index()
    .sort_values(by='Feeling of happiness (LV_Q46)', ascending=False)
    .rename(columns={'Feeling of happiness (NV_Q46)': 'Mode_FEC'})
)

Median_FEC = (
    DataFrame.groupby('Feeling of happiness (LV_Q46)')['Feeling of happiness (NV_Q46)']
    .agg(lambda x: x.median())
    .reset_index()
    .sort_values(by='Feeling of happiness (LV_Q46)', ascending=False)
    .rename(columns={'Feeling of happiness (NV_Q46)': 'Median_FEC'})
)


FoH_frequency_df=FoH_frequency_df.merge(Mode_FEC, how='left',on='Feeling of happiness (LV_Q46)')

FoH_frequency_df=FoH_frequency_df.merge(Median_FEC, how='left',on='Feeling of happiness (LV_Q46)')

FoH_frequency_df

# Create a violing plot
fig = px.violin(
    DataFrame,
    x='Feeling of happiness (LV_Q46)', 
    y='Satisfaction with financial situation of household (NV_Q50)',
    box=True,  # Add boxplot overlay
    points='all',  # Show all data points
    title="The value of Satisfaction with financial situation of household based on Feeling of happiness",
    hover_data=['Satisfaction with financial situation of household (NV_Q50)', 
                'Feeling of happiness (LV_Q46)']  # Cleaner hover data specification
)

# Update the order of categories
fig.update_layout(
    xaxis=dict(
        categoryorder='array', 
        categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
    ),
    plot_bgcolor='rgba(240, 240, 240, 1)',  # Light background color
    title_font=dict(size=24, family='Arial', color='black'),  # Title font styling
    xaxis_title='Feeling of happiness',
    yaxis_title='Satisfaction with financial situation of household',
    legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)

# Update the violins for styling
fig.update_traces(
    width=0.5,  # Thinner violins
    line_color='black',  # Black outline
    meanline_visible=True,  # Show mean line
    scalemode='count',  # Adjust width by the count of observations
    fillcolor='rgba(0,0,0,0)',  # Remove fill
)

# Show the plot
fig.show()

#pio.write_html(fig=fig, auto_open='True' ,file='xdplot.html')

DataFrame[['Feeling of happiness (NV_Q46)']].corrwith(DataFrame['Satisfaction with financial situation of household (NV_Q50)']).reset_index(name='Satisfaction with financial situation of household')

# mapping the old scale of Feeling o happiness with new scale 
def reverse_happiness(value):
    if pd.notnull(value):  # Ensure the value is not NaN
        mapping = {1: 4, 2: 3, 3: 2, 4: 1}
        return mapping.get(value, value)  # Return mapped value or the value itself if not in mapping
    return value
DataFrame['Feeling of happiness (NV_Q46)_reversed'] = DataFrame['Feeling of happiness (NV_Q46)'].apply(reverse_happiness)

DataFrame

DataFrame[['Feeling of happiness (NV_Q46)_reversed']].corrwith(DataFrame['Satisfaction with financial situation of household (NV_Q50)']).reset_index(name='Satisfaction with financial situation of household')

# Independent and dependent variables
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)']]  # Independent variable
y = DataFrame['Feeling of happiness (NV_Q46)_reversed']  # Ordinal dependent variable

# Fit the ordered logit model (ordinal regression)
model = OrderedModel(y, X, distr='logit')  # 'logit' specifies the logit link function
result = model.fit()

# Print the model summary
print(result.summary())

# Predict probabilities for each category
predictions = result.predict(X)

# Convert predictions to a NumPy array
predictions = predictions.to_numpy()

# Create a DataFrame to hold the predicted probabilities for each category
predictions_df = pd.DataFrame(predictions, columns=[f'Prob of Category {i+1}' for i in range(predictions.shape[1])])

# Add the satisfaction variable (X) to the DataFrame for plotting
predictions_df['Satisfaction with financial situation'] = DataFrame['Satisfaction with financial situation of household (NV_Q50)']

# Convert the DataFrame into long format for Plotly Express
predictions_long_df = predictions_df.melt(id_vars=['Satisfaction with financial situation'], 
                                        value_vars=[f'Prob of Category {i+1}' for i in range(predictions.shape[1])],
                                        var_name='Category',
                                        value_name='Probability')

# Map the happiness categories to human-readable labels
category_mapping = {0: 'Not at all happy',
                1: 'Not very happy',
                2: 'Quite happy',
                3: 'Very happy'}

# Extract category number from the 'Category' column and map to human-readable labels
predictions_long_df['Happiness Category'] = predictions_long_df['Category'].apply(
    lambda x: category_mapping[int(x.split()[-1])-1])  # map based on the category index (e.g. Category 1 -> Not at all happy)

# Plot the predicted probabilities using Plotly Express
fig = px.scatter(predictions_long_df, 
                x='Satisfaction with financial situation', 
                y='Probability', 
                color='Happiness Category',  # This column contains the mapped labels
                title='Ordinal Regression: Predicted Probabilities of Happiness based on Satisfaction with Financial Situation',
                labels={'Satisfaction with financial situation': 'Satisfaction with Financial Situation',
                        'Probability': 'Predicted Probability',
                        'Happiness Category': 'Happiness Level'})

# Update legend title
fig.update_layout(
    legend_title='Happiness Levels'  # This is the new title for the legend
)

# Show the plot
fig.show()

Optimization terminated successfully.
         Current function value: 0.740002
         Iterations: 162
         Function evaluations: 277
                                       OrderedModel Results                                       
==================================================================================================
Dep. Variable:     Feeling of happiness (NV_Q46)_reversed   Log-Likelihood:                -888.00
Model:                                       OrderedModel   AIC:                             1784.
Method:                                Maximum Likelihood   BIC:                             1804.
Date:                                    Sat, 29 Mar 2025                                         
Time:                                            16:23:41                                         
No. Observations:                                    1200                                         
Df Residuals:                                        1196                                         
Df Model:                                               1                                         
===============================================================================================================================
                                                                  coef    std err          z      P>|z|      [0.025      0.975]
-------------------------------------------------------------------------------------------------------------------------------
Satisfaction with financial situation of household (NV_Q50)     0.5065      0.034     14.780      0.000       0.439       0.574
1/2                                                            -2.0937      0.340     -6.157      0.000      -2.760      -1.427
2/3                                                             1.2139      0.090     13.491      0.000       1.038       1.390
3/4                                                             1.4424      0.033     43.198      0.000       1.377       1.508
===============================================================================================================================

# setupping the map
morocco_data = gpd.read_file("ma.json")
morocco_data.drop(columns='source',inplace=True)
# if the file was in goejson fiormat u need to load t as json format 
#morocco_geojson = json.loads(morocco_data.to_json())

morocco_data

labeled_df['N_REGION_ISO']

0       MA-09 Souss-Massa
1       MA-09 Souss-Massa
2       MA-09 Souss-Massa
3       MA-09 Souss-Massa
4       MA-09 Souss-Massa
              ...        
1195     MA-02 L'Oriental
1196     MA-02 L'Oriental
1197     MA-02 L'Oriental
1198     MA-02 L'Oriental
1199     MA-02 L'Oriental
Name: N_REGION_ISO, Length: 1200, dtype: object

# functoin to seperate the region id from its name
id_list = []
regions_name = []
y = labeled_df['N_REGION_ISO'].str.split(' ')
for x in y:
    id_list.append(x[0])
    regions_name.append(x[1])

# creating the dataframe the will be ploted 'chromaplot_df'
chromaplot_df = pd.DataFrame({
    'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
    'Feeling of happiness (NV_Q46)': df['Q46'],
    'Feeling of happiness (NV_Q46)_reversed': DataFrame['Feeling of happiness (NV_Q46)_reversed'],
    'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
    'N_REGION_ISO': labeled_df['N_REGION_ISO'],
    
})
chromaplot_df['ID_list'] = id_list
chromaplot_df['ID_list']= chromaplot_df['ID_list'].str.replace('-', '')

chromaplot_df['regions_name'] = regions_name

chromaplot_df

# calculating  the mean of Feeling of happiness for each region 
aggregated_data_FoH = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Feeling of happiness (NV_Q46)_reversed'].mean().reset_index()
aggregated_data_FoH

# calculating  the mean of Satisfaction with financial situation of household for each region 
aggregated_data_SFHS = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index()
aggregated_data_SFHS

# updating the chromaplot_df 
chromaplot_df = chromaplot_df.merge(aggregated_data_FoH, how='left', on='ID_list')
chromaplot_df = chromaplot_df.merge(aggregated_data_SFHS, how='left', on='ID_list')
chromaplot_df.rename(columns={
    'Feeling of happiness (NV_Q46)_reversed_x': 'Feeling of happiness (NV_Q46)_reversed',
    'Feeling of happiness (NV_Q46)_reversed_y': 'mean_FoH',
    'Satisfaction with financial situation of household (NV_Q50)_x':'Satisfaction with financial situation of household (NV_Q50)',
    'Satisfaction with financial situation of household (NV_Q50)_y':'mean_SFSH'
}, 
inplace=True)
chromaplot_df
# re-ordering the placement of the columns 
reorder=['ID_list','regions_name','N_REGION_ISO','Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','Feeling of happiness (NV_Q46)_reversed','mean_FoH','Satisfaction with financial situation of household (NV_Q50)','mean_SFSH']
chromaplot_df = chromaplot_df[reorder]
chromaplot_df

# grouping the values by ID_list
chromaplot_df.groupby('ID_list')[['regions_name','N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index()

#creating choropleth plots
fig = px.choropleth_mapbox(
    chromaplot_df,
    geojson=morocco_data,
    locations='ID_list',           # Unique region IDs in your DataFrame
    featureidkey="properties.id",  # Matches IDs in GeoJSON
    color='mean_FoH',  # Column to visualize
    hover_name='N_REGION_ISO',  # Column to display region name
    hover_data={
        'N_REGION_ISO' : True
    },   
    mapbox_style="carto-positron", # Base map style
    zoom=4,                        # Zoom level
    center={"lat": 31.7917, "lon": -7.0926},  # Center on Morocco
    color_continuous_scale="rdbu",  # Color scale for data visualization
    title="Average Feeling of Happiness in Morocco", # Map title
    range_color=[0, 4]
)
fig.update_layout(
    margin={"r":0,"t":30,"l":0,"b":0}  # Removes excess margins
)
fig.show()

#pio.write_html(fig, file='plot.html', auto_open=True)  # Save and open the plot in a browser

# showing the rating of the regions based on the Mean Feeling of happiness (reversed) for every region for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_FoH', ascending=False)

#creating choropleth plots
fig = px.choropleth_mapbox(
    chromaplot_df,
    geojson=morocco_data,
    locations='ID_list',           # Unique region IDs in your DataFrame
    featureidkey="properties.id",  # Matches IDs in GeoJSON
    color='mean_SFSH',  # Column to visualize
    hover_name='N_REGION_ISO',  # Column to display region name
    hover_data={
        'N_REGION_ISO' : True,
        'mean_FoH': True
    },   
    mapbox_style="carto-positron", # Base map style
    zoom=4,                        # Zoom level
    center={"lat": 31.7917, "lon": -7.0926},  # Center on Morocco
    color_continuous_scale="deep",  # Color scale for data visualization
    title="Average Satisfaction with financial situation of household in Morocco",# Map title
    range_color=[0,10]
    )
fig.update_layout(
    margin={"r":0,"t":30,"l":0,"b":0}  # Removes excess margins
)
fig.show()

#pio.write_html(fig, file='plot.html', auto_open=True)  # Save and open the plot in a browser

# showing the rating of the regions based on the Mean of Satisfaction with financial situation of household for every region 
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_SFSH', ascending=False)

	Variable Name	Label	Values
0	version	Version of Data File	{}
1	doi	Digital Object Identifier	{}
2	A_YEAR	Year of survey	{2017.0: '2017', 2018.0: '2018', 2019.0: '2019...
3	B_COUNTRY	ISO 3166-1 numeric country code	{504.0: 'Morocco'}
4	B_COUNTRY_ALPHA	ISO 3166-1 alpha-3 country code	{}
5	C_COW_NUM	CoW country code numeric	{600.0: 'Morocco'}
6	C_COW_ALPHA	CoW country code alpha	{}
7	D_INTERVIEW	Interview ID	{}
8	J_INTDATE	Date of interview	{-5.0: 'No answer; Missing; GB,NIRL: Postal su...
9	FW_START	Year/month of start-fieldwork	{}

	A_YEAR	B_COUNTRY	B_COUNTRY_ALPHA	C_COW_NUM	C_COW_ALPHA	D_INTERVIEW	J_INTDATE	FW_START	FW_END	K_TIME_START	K_TIME_END	K_DURATION	Q_MODE	N_REGION_ISO	N_REGION_WVS	N_TOWN	G_TOWNSIZE	G_TOWNSIZE2	H_SETTLEMENT	H_URBRURAL	I_PSU	O1_LONGITUDE	O2_LATITUDE	S_INTLANGUAGE	LNGE_ISO	E_RESPINT	F_INTPRIVACY	W_WEIGHT	S018	PWGHT	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22	Q23	Q24	Q25	Q26	Q27	Q28	Q29	Q30	Q31	Q32	Q33	Q33_3	Q34	Q34_3	Q35	Q35_3	Q37	Q38	Q39	Q40	Q41	Q42	Q43	Q44	Q45	Q46	Q47	Q48	Q49	Q50	Q51	Q52	Q53	Q54	Q55	Q56	Q57	Q58	Q59	Q60	Q61	Q62	Q63	Q64	Q65	Q66	Q67	Q68	Q69	Q70	Q71	Q72	Q73	Q74	Q75	Q76	Q77	Q78	Q79	Q80	Q81	Q82	Q82_ARABLEAGUE	Q82_GULFCOOP	Q82_ISLCOOP	Q83	Q84	Q85	Q86	Q87	Q88	Q89	Q90	Q91	Q92	Q93	Q94	Q95	Q96	Q97	Q98	Q99	Q100	Q101	Q102	Q103	Q104	Q105	Q106	Q107	Q108	Q109	Q110	Q111	Q112	Q113	Q114	Q115	Q116	Q117	Q118	Q119	Q120	Q121	Q122	Q123	Q124	Q125	Q126	Q127	Q128	Q129	Q130	Q131	Q132	Q133	Q134	Q135	Q136	Q137	Q138	Q139	Q140	Q141	Q142	Q143	Q144	Q145	Q146	Q147	Q148	Q149	Q150	Q151	Q152	Q153	Q154	Q155	Q156	Q157	Q158	Q159	Q160	Q161	Q162	Q163	Q164	Q165	Q166	Q167	Q168	Q169	Q170	Q171	Q172	Q173	Q174	Q175	Q176	Q177	Q178	Q179	Q180	Q181	Q182	Q183	Q184	Q185	Q186	Q187	Q188	Q189	Q190	Q191	Q192	Q193	Q194	Q195	Q196	Q197	Q198	Q199	Q200	Q201	Q202	Q203	Q204	Q205	Q206	Q207	Q208	Q209	Q210	Q211	Q212	Q213	Q214	Q215	Q216	Q217	Q218	Q219	Q220	Q221	Q222	Q223	Q223_ABREV	Q223_LOCAL	Q224	Q225	Q226	Q227	Q228	Q229	Q230	Q231	Q232	Q233	Q234	Q234A	Q235	Q236	Q237	Q238	Q239	Q240	Q241	Q242	Q243	Q244	Q245	Q246	Q247	Q248	Q249	Q250	Q251	Q252	Q253	Q254	Q255	Q256	Q257	Q258	Q259	Q260	Q261	Q262	X003R	X003R2	Q263	Q264	Q265	Q266	Q267	Q268	Q269	Q270	Q271	Q272	Q273	Q274	Q275	Q275R	Q276	Q276R	Q277	Q277R	Q278	Q278R	Q279	Q280	Q281	Q282	Q283	Q284	Q285	Q286	Q287	Q288	Q288R	Q289	Q289CS9	Q290	Q291G1	Q291G2	Q291G3	Q291G4	Q291G5	Q291G6	Q291P1	Q291P2	Q291P3	Q291P4	Q291P5	Q291P6	Q291UN1	Q291UN2	Q291UN3	Q291UN4	Q291UN5	Q291UN6	Q292A	Q292B	Q292C	Q292D	Q292E	Q292F	Q292G	Q292H	Q292I	Q292J	Q292K	Q292L	Q292M	Q292N	Q292O	Q293	Q294A	Q294B	Y001	Y002	Y003	SACSECVAL	SACSECVALB	RESEMAVAL	RESEMAVALB	I_AUTHORITY	I_NATIONALISM	I_DEVOUT	DEFIANCE	I_RELIGIMP	I_RELIGBEL	I_RELIGPRAC	DISBELIEF	I_NORM1	I_NORM2	I_NORM3	RELATIVISM	I_TRUSTARMY	I_TRUSTPOLICE	I_TRUSTCOURTS	SCEPTICISM	I_INDEP	I_IMAGIN	I_NONOBED	AUTONOMY	I_WOMJOB	I_WOMPOL	I_WOMEDU	EQUALITY	I_HOMOLIB	I_ABORTLIB	I_DIVORLIB	CHOICE	I_VOICE1	I_VOICE2	I_VOI2_00	VOICE	SECVALWGT	WEIGHT1A	WEIGHT1B	WEIGHT2A	WEIGHT2B	WEIGHT3A	WEIGHT3B	WEIGHT4A	WEIGHT4B	RESEMAVALBWGT	RESEMAVALWGT	SECVALBWGT	Y001_1	Y001_3	Y001_4	Y001_5
0	2021	Morocco	MAR	Morocco	MOR	504070001.0	20211112.0	202111.0	202112.0	10.05	10.43	38.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were other people around who could follo...	No weighting	0.833333	31083.33333	Very important	Rather important	Very important	Very important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Important	Not mentioned	Important	Mentioned	Not mentioned	Not mentioned	Mentioned	Mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Agree strongly	Agree	Agree strongly	Disagree	Agree strongly	Agree strongly	Agree	Agree	Agree strongly	Agree	Strongly agree	Agree	Agree	Agree strongly	Strongly agree	Strongly agree	Neither agree nor disagree	Our society must be gradually improved by reforms	Don't mind	Good thing	Bad thing	Quite happy	Fair	5	8	6	Never	Rarely	Never	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Trust somewhat	Do not trust at all	Do not trust at all	Trust somewhat	Quite a lot	Not very much	Not very much	Quite a lot	Not very much	Not very much	A great deal	None at all	None at all	None at all	Not very much	Quite a lot	A great deal	A great deal	Not very much	A great deal	Quite a lot	Not very much	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Quite a lot	Not very much	Quite a lot	Not very much	None at all	None at all	Quite a lot	5	India	Geneva	Human rights	Inactive member	Don't belong	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	6	4	5	5	5	Economy growth and creating jobs	10 There is abundant corruption in my country	Most of them	Most of them	Few of them	Few of them	Most of them	Frequently	Disagree	8	Neither good, nor bad	Agree	Agree	Hard to say	Agree	Hard to say	Agree	Hard to say	Disagree	Place strict limits on the number of foreigner...	Quite secure	Not frequently	Not frequently	Quite frequently	Quite frequently	Not frequently	Not frequently	Not frequently	Yes	No	Yes	A great deal	A great deal	No	No	Very much	Very much	A great deal	Equality	Security	No	A high level of economic growth	Seeing that people have more say about how ar...	Maintaining order in the nation	Protecting freedom of speech	Progress toward a less impersonal and more hum...	The fight against crime	6	8	8	5	6	8	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	Not a religious person	Do good to other people	Make sense of life after death	4	5	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	7	2	Never justifiable	6	2	8	Never justifiable	Never justifiable	Never justifiable	3	Always justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Never	Never	Daily	Weekly	Weekly	Less than monthly	Weekly	Weekly	Never	Have done	Have done	Would never do	Would never do	Might do	Would never do	Might do	Would never do	Might do	Would never do	Would never do	Would never do	Usually	Usually	MAR: Authenticity and Modernity Party	MAR: PAM	MAR: Parti de l'Authenticite et de la Modernit...	Very often	Not often	Fairly often	Very often	Very often	Very often	Very often	Not at all often	Not at all often	Not often	Rather important	Not at all	Very bad	Very bad	Very bad	Fairly good	Very good	8	An essential characteristic of democracy	7	6	9	4	8	9	5	An essential characteristic of democracy	Absolutely important	3	Not satisfied at all	Not much respect	Not very proud	Not close at all	Not close at all	Not close at all	Not very close	Not close at all	Female	1969.0	52.0	45-54	50 and more years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	8.0	No	Berber; Amazigh;Tamaziɣt	Married	4 children	Primary education (ISCED 1)	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Full time (30 hours a week or more)	Homemaker not otherwise employed	Service (for example: restaurant owner, police...	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Private business or industry	Yes	Spent some savings	Upper middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Agree	Disagree strongly	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Agree	Disagree strongly	Disagree	Disagree strongly	Disagree	Neither agree nor disagree	Disagree	Disagree	Agree	Disagree strongly	Neither agree nor disagree	Agree strongly	Agree	Neither agree nor disagree	Disagree	Disagree	Agree	Disagree	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree	Disagree strongly	Neither agree nor disagree	1	1	1	3	Mixed	Obedience/Religious Faith	0.331667	0.443333	0.235139	0.262778	High	High	Very low	0.553333	Very low	Not religious or atheist	0.000000	0.333333	Conformist	Conformist	Conformist	0.000000	Low	Low	Very high	0.44	No	No	No	0.000000	Medium-Low	Very low	High	0.303333	0.000000	0.000000	0.666667	0.222222	High	Medio	0.415	0.415	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	1	1	1	0
1	2021	Morocco	MAR	Morocco	MOR	504070002.0	20211112.0	202111.0	202112.0	10.46	11.19	33.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Rather important	Not very important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Agree strongly	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Disagree strongly	Disagree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	Our present society must be valiantly defended...	Bad thing	Good thing	Good thing	Not at all happy	Poor	5	4	2	Never	Often	Often	Sometimes	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust very much	A great deal	A great deal	None at all	None at all	Not very much	A great deal	Not very much	None at all	None at all	None at all	Not very much	Not very much	None at all	None at all	None at all	Quite a lot	Quite a lot	A great deal	Not very much	Not very much	Not very much	Quite a lot	None at all	None at all	None at all	None at all	None at all	None at all	None at all	5	India	London	Human rights	Active member	Don't belong	Active member	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	9	6	6	Protecting environment	10 There is abundant corruption in my country	Most of them	All of them	Most of them	None of them	All of them	Always	Hard to say	Very high risk	Rather bad	Agree	Agree	Disagree	Disagree	Agree	Agree	Agree	Agree	Prohibit people coming here from other countries	Quite secure	Very Frequently	Very Frequently	Very Frequently	Very Frequently	Quite frequently	Very Frequently	Very Frequently	Yes	Yes	No	Very much	Very much	Yes	Yes	Very much	Very much	Very much	Freedom	Security	Yes	A high level of economic growth	Trying to make our cities and countryside more...	Maintaining order in the nation	Fighting rising prices	Progress toward a less impersonal and more hum...	Progress toward a society in which Ideas count...	Completely agree	Completely agree	Completely agree	Completely agree	8	A lot worse off	Not at all important	Yes	No	No	No	Strongly disagree	Strongly disagree	Once a year	Only on special holy days	A religious person	Do good to other people	Make sense of life in this world	5	5	5	5	5	5	5	5	5	8	5	5	5	5	9	5	5	5	5	6	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Occasionally	Less than monthly	Less than monthly	Never	Daily	Never	Daily	Daily	Less than monthly	Might do	Have done	Might do	Might do	Have done	Might do	Might do	Would never do	Might do	Might do	Would never do	Would never do	Never	Never	MAR: Justice and Development Party	MAR: PJD	MAR: Parti de la Justice et du Developpement -...	Not at all often	Not often	Not at all often	Not at all often	Not at all often	Not at all often	Very often	Very often	Not often	Fairly often	Very important	Not at all	Very good	Very good	Very bad	Very good	Very good	Left	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	5	8	6	7	Not much respect	Very proud	Very close	Very close	Close	Not very close	Not close at all	Female	1998.0	23.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	6	Yes, own parent(s)	Berber; Amazigh;Tamaziɣt	Single	No children	Bachelor or equivalent (ISCED 6)	Higher	Upper secondary education (ISCED 3)	Middle	Early childhood education (ISCED 0) / no educa...	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Full time (30 hours a week or more)	NaN	Sales (for example: sales manager, shop owner,...	NaN	Skilled worker (for example: foreman, motor me...	Private non-profit organization	No	Just get by	Working class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Agree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree strongly	Agree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Disagree strongly	Neither agree nor disagree	Neither agree nor disagree	Neither agree nor disagree	Agree	Agree strongly	Agree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree	Disagree	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Disagree strongly	Agree strongly	Disagree strongly	Disagree strongly	No trust at all	NaN	NaN	2	Materialist	-1	0.360556	0.111111	0.360556	0.387778	Low	Very low	Very low	0.000000	Very low	Religious	0.666667	0.222222	Inconformist	Inconformist	Inconformist	1.000000	Very high	Very high	Low	0.22	No	Yes	Yes	0.666667	Low	Very low	High	0.220000	0.444444	0.444444	0.777778	0.555556	Very high	Bajo	0.000	0.000	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	1	1
2	2021	Morocco	MAR	Morocco	MOR	504070003.0	20211112.0	202111.0	202112.0	11.28	12.09	41.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were other people around who could follo...	No weighting	0.833333	31083.33333	Very important	Rather important	Very important	Not at all important	Very important	Very important	Important	Not mentioned	Important	Important	Not mentioned	Important	Not mentioned	Important	Important	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Agree	Disagree	Neither agree nor disagree	Neither agree nor disagree	Agree strongly	Agree	Strongly agree	Agree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	The entire way our society is organized must b...	Bad thing	Good thing	Good thing	Not very happy	Poor	2	2	3	Rarely	Rarely	Sometimes	Often	Often	Worse off	Need to be very careful	Trust completely	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust at all	Do not trust at all	Quite a lot	A great deal	Quite a lot	Not very much	None at all	A great deal	A great deal	Not very much	Not very much	None at all	A great deal	Quite a lot	None at all	A great deal	A great deal	A great deal	Not very much	Not very much	None at all	None at all	None at all	None at all	Not very much	None at all	None at all	None at all	A great deal	Quite a lot	A great deal	9	India	Geneva	Human rights	Don't belong	Active member	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	Incomes should be made more equal	Private ownership of business should be increased	The government should take more responsibility...	Competition is good	In the long run, hard work usually brings a be...	Protecting environment	10 There is abundant corruption in my country	Few of them	Most of them	Few of them	Few of them	Most of them	Always	Strongly disagree	Very high risk	Quite good	Disagree	Agree	Agree	Agree	Disagree	Agree	Agree	Disagree	Let people come as long as there are jobs avai...	Quite secure	Not frequently	Not at all frequently	Not at all frequently	Quite frequently	Not frequently	Not frequently	Very Frequently	Yes	Yes	No	Not at all	A great deal	Yes	Yes	Very much	Very much	Very much	Equality	Freedom	Yes	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Protecting freedom of speech	Progress toward a less impersonal and more hum...	Progress toward a society in which Ideas count...	8	8	7	5	2	7	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	Once a week	Several times a day	Not a religious person	Follow religious norms and ceremonies	Make sense of life after death	4	7	2	2	2	Never justifiable	Never justifiable	Never justifiable	4	7	2	Never justifiable	Never justifiable	5	3	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Definitely should have the right	Probably should not have the right	Probably should not have the right	Not at all interested	Never	Never	Never	Never	Never	Never	Never	Weekly	Weekly	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Might do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Very often	Not at all often	Fairly often	Not often	Not often	Very often	Fairly often	Not often	Not often	Very often	Not very important	Very little	Very good	Very good	Fairly bad	Fairly good	Fairly good	8	An essential characteristic of democracy	7	An essential characteristic of democracy	An essential characteristic of democracy	5	An essential characteristic of democracy	6	8	An essential characteristic of democracy	Absolutely important	6	6	Fairly much respect	Quite proud	Very close	Close	Not close at all	Not close at all	Not close at all	Male	1986.0	35.0	35-44	30-49 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	Yes, own parent(s)	Arabic	Divorced	1 child	Bachelor or equivalent (ISCED 6)	Higher	Post-secondary non-tertiary education (ISCED 4)	Middle	Bachelor or equivalent (ISCED 6)	Higher	Bachelor or equivalent (ISCED 6)	Higher	Unemployed	NaN	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Farm worker (for example: farm labourer, tract...	Private non-profit organization	Yes	Just get by	Upper middle class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Disagree strongly	Disagree	Agree strongly	Disagree	Disagree	Agree strongly	Disagree strongly	Disagree strongly	Agree	Disagree	Disagree	Agree	Agree	Neither agree nor disagree	Agree	Neither agree nor disagree	Agree	Agree strongly	Agree strongly	Agree strongly	Neither agree nor disagree	Disagree	Agree	Agree strongly	Disagree strongly	Neither agree nor disagree	Agree strongly	Agree	Disagree strongly	Agree	Agree	Agree	Disagree	7	NaN	NaN	3	Mixed	0	0.291389	0.249444	0.304583	0.360000	Low	Low	Very low	0.110000	Very low	Not religious or atheist	0.166667	0.388889	Inconformist	Inconformist	Conformist	0.666667	Very high	Very high	Very high	0.00	No	No	Yes	0.333333	Medium	Very low	High	0.386667	0.000000	0.333333	0.666667	0.333333	High	Bajo	0.165	0.165	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	1	1	1
3	2021	Morocco	MAR	Morocco	MOR	504070004.0	20211112.0	202111.0	202112.0	12.08	12.42	34.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Very important	Not at all important	Rather important	Very important	Very important	Important	Not mentioned	Important	Not mentioned	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Important	Important	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Agree strongly	Disagree	Disagree	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Strongly agree	Agree	Agree strongly	Agree strongly	Strongly agree	Strongly agree	Agree strongly	Our present society must be valiantly defended...	Bad thing	Good thing	Good thing	Quite happy	Very good	8	Completely dissatisfied	6	Never	Rarely	Never	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Do not trust very much	Do not trust at all	Do not trust very much	Do not trust very much	Not very much	A great deal	None at all	None at all	None at all	A great deal	A great deal	Not very much	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	None at all	Not very much	Not very much	Not very much	Not very much	Not very much	None at all	None at all	Not very much	None at all	None at all	None at all	Being democratic	India	Geneva	Human rights	Don't belong	Don't belong	Don't belong	Not a member	Not a member	Don't belong	Not a member	Not a member	Don't belong	Don't belong	Don't belong	Don't belong	There should be greater incentives for individ...	Government ownership of business should be inc...	People should take more responsibility to prov...	Competition is harmful	Hard work doesn't generally bring success - it...	Economy growth and creating jobs	7	Few of them	Most of them	Few of them	Few of them	Most of them	Frequently	Strongly agree	Very high risk	Quite good	Agree	Disagree	Hard to say	Hard to say	Disagree	Disagree	Agree	Disagree	Place strict limits on the number of foreigner...	Very Secure	Not frequently	Quite frequently	Quite frequently	Not at all frequently	Quite frequently	Not frequently	Very Frequently	Yes	Yes	Yes	Very much	Very much	No	No	Very much	Very much	Very much	Equality	Freedom	Yes	A high level of economic growth	Trying to make our cities and countryside more...	Maintaining order in the nation	Fighting rising prices	A stable economy	Progress toward a society in which Ideas count...	Completely agree	Completely agree	Completely agree	8	Completely disagree	A lot better off	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	A religious person	Do good to other people	Make sense of life in this world	Completely agree	5	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Somewhat interested	Occasionally	Less than monthly	Daily	Less than monthly	Daily	Daily	Daily	Daily	Daily	Would never do	Have done	Would never do	Would never do	Have done	Might do	Have done	Have done	Have done	Have done	Have done	Have done	Usually	Usually	None	4.0	4.0	Not often	Not often	Very often	Very often	Not often	Fairly often	Fairly often	Fairly often	Very often	Fairly often	Very important	Some	Very good	Very bad	Very bad	Very bad	Bad	5	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	Not an essential characteristic of democracy	An essential characteristic of democracy	An essential characteristic of democracy	Not an essential characteristic of democracy	Not an essential characteristic of democracy	Absolutely important	6	6	A great deal of respect	Very proud	Close	Not close at all	Not close at all	Not close at all	Not close at all	Male	1993.0	28.0	25-34	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	4	Yes, own parent(s)	Arabic	Married	2 children	Primary education (ISCED 1)	Lower	Early childhood education (ISCED 0) / no educa...	Lower	Lower secondary education (ISCED 2)	Lower	Lower secondary education (ISCED 2)	Lower	Unemployed	Full time (30 hours a week or more)	Clerical (for example: secretary, clerk, offic...	Never had a job	Farm worker (for example: farm labourer, tract...	Private business or industry	No	Just get by	Lower middle class	Sixth step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Agree strongly	Agree strongly	Agree	Disagree strongly	Neither agree nor disagree	Disagree	Agree strongly	Agree strongly	Agree	Disagree strongly	Disagree	Agree strongly	Agree strongly	Agree strongly	Neither agree nor disagree	Disagree	Disagree	Agree	Agree	Agree	Agree	Agree	Disagree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	Agree	5	NaN	NaN	1	Materialist	-1	0.000000	0.000000	0.055000	0.110000	Low	Very low	Very low	0.000000	Very low	Religious	0.000000	0.000000	Conformist	Conformist	Conformist	0.000000	Very high	Very high	Very high	0.00	No	No	No	0.000000	Low	Very low	High	0.220000	0.000000	0.000000	0.000000	0.000000	Very high	Bajo	0.000	0.000	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	1
4	2021	Morocco	MAR	Morocco	MOR	504070005.0	20211112.0	202111.0	202112.0	13.18	13.55	37.0	Paper-and-Pencil Interviewing (PAPI)	MA-09 Souss-Massa	MA: MA-09 Souss-Massa	MA: Afella Ighir	Under 2,000	Under 5,000	Another city, town (not a regional or district...	Rural	99.0	-8.84	29.49	Arabic	ar	Respondent was very interested	There were no other people around who could fo...	No weighting	0.833333	31083.33333	Very important	Rather important	Not very important	Not very important	Very important	Very important	Important	Important	Not mentioned	Important	Not mentioned	Not mentioned	Not mentioned	Important	Not mentioned	Important	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Mentioned	Not mentioned	Not mentioned	Agree strongly	Agree strongly	Disagree	Disagree	Agree	Agree strongly	Agree strongly	Agree	Agree strongly	Agree	Neither agree nor disagree	Neither agree nor disagree	Neither agree nor disagree	Agree	Disagree	Disagree	Neither agree nor disagree	The entire way our society is organized must b...	Bad thing	Don't mind	Don't mind	Not very happy	Good	7	7	4	Sometimes	Rarely	Sometimes	Never	Never	Or about the same	Need to be very careful	Trust completely	Do not trust very much	Trust somewhat	Do not trust very much	Do not trust very much	Do not trust very much	Quite a lot	Quite a lot	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	None at all	Not very much	None at all	None at all	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	Not very much	5	China	Washington DC	Destruction of historic monuments	Inactive member	Active member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Inactive member	Don't belong	6	5	3	Competition is good	5	Protecting environment	8	Most of them	Most of them	Most of them	Most of them	Most of them	Frequently	Strongly disagree	Very high risk	Neither good, nor bad	Agree	Hard to say	Hard to say	Hard to say	Disagree	Agree	Disagree	Hard to say	Let people come as long as there are jobs avai...	Very Secure	Quite frequently	Quite frequently	Not at all frequently	Quite frequently	Quite frequently	Quite frequently	Very Frequently	Yes	No	No	A great deal	Very much	No	No	Very much	Very much	Very much	Equality	Security	No	A high level of economic growth	Making sure this country has strong defence fo...	Maintaining order in the nation	Fighting rising prices	A stable economy	Progress toward a less impersonal and more hum...	8	Completely agree	7	6	Completely disagree	8	Very important	Yes	Yes	Yes	Yes	Strongly agree	Strongly agree	More than once a week	Several times a day	Not a religious person	Follow religious norms and ceremonies	Make sense of life after death	7	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	Never justifiable	3	Never justifiable	4	Never justifiable	8	2	Never justifiable	4	5	Always justifiable	Definitely should have the right	Definitely should not have the right	Definitely should not have the right	Not at all interested	Never	Never	Never	Never	Never	Never	Daily	Daily	Monthly	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Would never do	Might do	Would never do	Would never do	Would never do	Never	Never	None	4.0	4.0	Very often	Fairly often	Not at all often	Fairly often	Fairly often	Very often	Fairly often	Not often	Fairly often	Very often	Not very important	Very little	Very bad	Fairly bad	Very bad	Very good	Very good	7	Not an essential characteristic of democracy	3	5	9	6	An essential characteristic of democracy	An essential characteristic of democracy	3	9	8	5	3	Not much respect	Not very proud	Close	Close	Not very close	Not very close	Not close at all	Male	1999.0	22.0	16-24	16-29 years	I am born in this country	Not an immigrant	Not an immigrant	Morocco	Morocco	Morocco	Yes	5	No	Arabic	Single	No children	Primary education (ISCED 1)	Lower	NaN	NaN	Upper secondary education (ISCED 3)	Middle	Lower secondary education (ISCED 2)	Lower	Full time (30 hours a week or more)	NaN	Service (for example: restaurant owner, police...	NaN	Farm worker (for example: farm labourer, tract...	Private non-profit organization	No	Spent some savings	Working class	Seventh step	Medium	Muslim	Islam; nfd	MA: Arabe	Agree	Disagree	Agree strongly	Agree strongly	Disagree	Disagree strongly	Disagree	Disagree	Agree	Agree	Disagree	Agree	Disagree	Disagree	Agree strongly	Disagree	Disagree	Disagree	Agree strongly	Agree strongly	Disagree	Disagree	Agree	Agree strongly	Disagree strongly	Disagree strongly	Agree strongly	Agree strongly	Disagree strongly	Agree strongly	Agree strongly	Agree	Agree strongly	2	1	1	1	Materialist	Determination, perseverance/Independence	0.317500	0.360000	0.276667	0.220000	Medium	High	Very low	0.386667	Very low	Not religious or atheist	0.000000	0.333333	Conformist	Conformist	Conformist	0.000000	High	Low	Low	0.55	Yes	No	Yes	0.666667	Low	High	High	0.440000	0.000000	0.000000	0.000000	0.000000	Very high	Bajo	0.000	0.000	1.0	Complete	Complete	Complete	Complete	Complete	Complete	Complete	Complete	1.0	1.0	1.0	0	0	0	1

	Variable Name	Label	Values
82	Q49	Satisfaction with your life	{-5.0: 'Other missing; Multiple answers Mail (...
83	Q50	Satisfaction with financial situation of house...	{-5.0: 'Missing; Unknown', -4.0: 'Not asked', ...
291	Q252	Satisfaction with the political system perform...	{-5.0: 'Other missing; Multiple answers Mail (...

	id	name	geometry
0	MA11	Laâyoune-Sakia El Hamra	POLYGON ((-8.75659 27.14772, -8.75422 27.14715...
1	MA01	Tangier-Tetouan-Al Hoceima	POLYGON ((-3.82389 35.19936, -3.82346 35.19887...
2	MA02	Oriental	POLYGON ((-3.82012 34.88753, -3.81998 34.89199...
3	MA08	Drâa-Tafilalet	POLYGON ((-4.01816 32.60844, -4.00295 32.57946...
4	MA09	Souss-Massa	POLYGON ((-7.73179 31.13463, -7.73274 31.13365...
5	MA10	Guelmim-Oued Noun	POLYGON ((-8.75659 27.14772, -8.80915 27.16043...
6	MA06	Casablanca-Settat	POLYGON ((-9.05932 32.72101, -9.05904 32.7215,...
7	MA07	Marrakech-Safi	POLYGON ((-7.18452 31.42896, -7.1929 31.42544,...
8	MA12	Dakhla-Oued Ed-Dahab	POLYGON ((-14.90488 24.6832, -14.89861 24.6739...
9	MA04	Rabat-Salé-Kenitra	POLYGON ((-6.2434 35.00139, -6.23397 34.99872,...
10	MA03	Fez-Meknes	POLYGON ((-5.31515 34.5159, -5.28082 34.51478,...
11	MA05	Béni Mellal-Khénifra	POLYGON ((-5.25455 32.86937, -5.25284 32.86344...

	ID_list	N_REGION_ISO	Feeling of happiness (NV_Q46)_reversed
0	MA01	MA-01 Tanger-Tetouan-Al Hoceima	2.941667
1	MA02	MA-02 L'Oriental	3.137500
2	MA03	MA-03 Fes-Meknes	3.020000
3	MA04	MA-04 Rabat-Sale-Kenitra	2.911765
4	MA05	MA-05 Beni Mellal-Khenifra	2.812500
5	MA06	MA-06 Casablanca-Settat	2.934615
6	MA07	MA-07 Marrakech-Safi	2.893333
7	MA08	MA-08 Draa-Tafilalet	2.980000
8	MA09	MA-09 Souss-Massa	2.940000
9	MA10	MA-10 Guelmim-Oued Noun	2.900000
10	MA11	MA-11 Laayoune-Sakia El Hamra (EH-partial)	2.800000
11	MA12	MA-12 Dakhla-Oued Ed-Dahab (EH)	3.000000

Analazing the impact of Satisfaction with financial situation of household effect Feeling of happiness¶

Introduction:¶

Project Overview¶

Methodology¶

Key Steps¶

Fixing the "Feeling of Happiness" Scale¶

What Does a Correlation of 0.44 Mean?¶

Regression and Why I Use Ordinal Regression¶

Why Use Ordinal Regression¶

Why Not Linear or Logistic Regression?¶

Interpretation of the Ordinal Regression Results¶

What Was Tested¶

Key Results¶

Overall Interpretation¶

Exploring Regional Differences¶

Mean Feeling of happiness (reversed) for every region¶

showing the rating of the regions based on mean of Satisfaction with financial situation of household for every region¶

Regional Differences in the Relationship Between Financial Satisfaction and Happiness¶

	version	doi	A_YEAR	B_COUNTRY	B_COUNTRY_ALPHA	C_COW_NUM	C_COW_ALPHA	D_INTERVIEW	J_INTDATE	FW_START	FW_END	K_TIME_START	K_TIME_END	K_DURATION	Q_MODE	N_REGION_ISO	N_REGION_WVS	N_TOWN	G_TOWNSIZE	G_TOWNSIZE2	H_SETTLEMENT	H_URBRURAL	I_PSU	O1_LONGITUDE	O2_LATITUDE	S_INTLANGUAGE	LNGE_ISO	E_RESPINT	F_INTPRIVACY	W_WEIGHT	S018	PWGHT	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22	Q23	Q24	Q25	Q26	Q27	Q28	Q29	Q30	Q31	Q32	Q33	Q33_3	Q34	Q34_3	Q35	Q35_3	Q37	Q38	Q39	Q40	Q41	Q42	Q43	Q44	Q45	Q46	Q47	Q48	Q49	Q50	Q51	Q52	Q53	Q54	Q55	Q56	Q57	Q58	Q59	Q60	Q61	Q62	Q63	Q64	Q65	Q66	Q67	Q68	Q69	Q70	Q71	Q72	Q73	Q74	Q75	Q76	Q77	Q78	Q79	Q80	Q81	Q82	Q82_ARABLEAGUE	Q82_GULFCOOP	Q82_ISLCOOP	Q83	Q84	Q85	Q86	Q87	Q88	Q89	Q90	Q91	Q92	Q93	Q94	Q95	Q96	Q97	Q98	Q99	Q100	Q101	Q102	Q103	Q104	Q105	Q106	Q107	Q108	Q109	Q110	Q111	Q112	Q113	Q114	Q115	Q116	Q117	Q118	Q119	Q120	Q121	Q122	Q123	Q124	Q125	Q126	Q127	Q128	Q129	Q130	Q131	Q132	Q133	Q134	Q135	Q136	Q137	Q138	Q139	Q140	Q141	Q142	Q143	Q144	Q145	Q146	Q147	Q148	Q149	Q150	Q151	Q152	Q153	Q154	Q155	Q156	Q157	Q158	Q159	Q160	Q161	Q162	Q163	Q164	Q165	Q166	Q167	Q168	Q169	Q170	Q171	Q172	Q173	Q174	Q175	Q176	Q177	Q178	Q179	Q180	Q181	Q182	Q183	Q184	Q185	Q186	Q187	Q188	Q189	Q190	Q191	Q192	Q193	Q194	Q195	Q196	Q197	Q198	Q199	Q200	Q201	Q202	Q203	Q204	Q205	Q206	Q207	Q208	Q209	Q210	Q211	Q212	Q213	Q214	Q215	Q216	Q217	Q218	Q219	Q220	Q221	Q222	Q223	Q223_ABREV	Q223_LOCAL	Q224	Q225	Q226	Q227	Q228	Q229	Q230	Q231	Q232	Q233	Q234	Q234A	Q235	Q236	Q237	Q238	Q239	Q240	Q241	Q242	Q243	Q244	Q245	Q246	Q247	Q248	Q249	Q250	Q251	Q252	Q253	Q254	Q255	Q256	Q257	Q258	Q259	Q260	Q261	Q262	X003R	X003R2	Q263	Q264	Q265	Q266	Q267	Q268	Q269	Q270	Q271	Q272	Q273	Q274	Q275	Q275R	Q276	Q276R	Q277	Q277R	Q278	Q278R	Q279	Q280	Q281	Q282	Q283	Q284	Q285	Q286	Q287	Q288	Q288R	Q289	Q289CS9	Q290	Q291G1	Q291G2	Q291G3	Q291G4	Q291G5	Q291G6	Q291P1	Q291P2	Q291P3	Q291P4	Q291P5	Q291P6	Q291UN1	Q291UN2	Q291UN3	Q291UN4	Q291UN5	Q291UN6	Q292A	Q292B	Q292C	Q292D	Q292E	Q292F	Q292G	Q292H	Q292I	Q292J	Q292K	Q292L	Q292M	Q292N	Q292O	Q293	Q294A	Q294B	Y001	Y002	Y003	SACSECVAL	SACSECVALB	RESEMAVAL	RESEMAVALB	I_AUTHORITY	I_NATIONALISM	I_DEVOUT	DEFIANCE	I_RELIGIMP	I_RELIGBEL	I_RELIGPRAC	DISBELIEF	I_NORM1	I_NORM2	I_NORM3	RELATIVISM	I_TRUSTARMY	I_TRUSTPOLICE	I_TRUSTCOURTS	SCEPTICISM	I_INDEP	I_IMAGIN	I_NONOBED	AUTONOMY	I_WOMJOB	I_WOMPOL	I_WOMEDU	EQUALITY	I_HOMOLIB	I_ABORTLIB	I_DIVORLIB	CHOICE	I_VOICE1	I_VOICE2	I_VOI2_00	VOICE	SECVALWGT	WEIGHT1A	WEIGHT1B	WEIGHT2A	WEIGHT2B	WEIGHT3A	WEIGHT3B	WEIGHT4A	WEIGHT4B	RESEMAVALBWGT	RESEMAVALWGT	SECVALBWGT	Y001_1	Y001_2	Y001_3	Y001_4	Y001_5
0	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070001.0	20211112.0	202111.0	202112.0	10.05	10.43	38.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	3.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	3.0	2.0	2.0	1.0	3.0	2.0	3.0	5.0	8.0	6.0	4.0	3.0	4.0	4.0	4.0	3.0	2.0	1.0	3.0	2.0	4.0	4.0	2.0	2.0	3.0	3.0	2.0	3.0	3.0	1.0	4.0	4.0	4.0	3.0	2.0	1.0	1.0	3.0	1.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	2.0	3.0	4.0	4.0	2.0	5.0	3.0	3.0	2.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	6.0	4.0	5.0	5.0	5.0	2.0	10.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	8.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	0.0	3.0	2.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	3.0	1.0	4.0	2.0	4.0	6.0	8.0	8.0	5.0	6.0	8.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	2.0	1.0	4.0	5.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	2.0	1.0	6.0	2.0	8.0	1.0	1.0	1.0	3.0	10.0	1.0	4.0	4.0	2.0	3.0	5.0	1.0	2.0	2.0	4.0	2.0	2.0	5.0	1.0	1.0	3.0	3.0	2.0	3.0	2.0	3.0	2.0	3.0	3.0	3.0	2.0	2.0	504011.0	504011.0	504011.0	1.0	3.0	2.0	1.0	1.0	1.0	1.0	4.0	4.0	3.0	2.0	5.0	4.0	4.0	4.0	2.0	1.0	8.0	10.0	7.0	6.0	9.0	4.0	8.0	9.0	5.0	10.0	10.0	3.0	1.0	3.0	3.0	4.0	4.0	4.0	3.0	4.0	2.0	1969.0	52.0	4.0	3.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	8.0	1.0	500.0	1.0	4.0	1.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	5.0	5.0	9.0	9.0	2.0	1.0	3.0	2.0	6.0	2.0	5.0	50000000.0	504005.0	5.0	2.0	5.0	4.0	5.0	5.0	5.0	2.0	5.0	4.0	5.0	4.0	3.0	4.0	4.0	2.0	5.0	3.0	5.0	4.0	3.0	2.0	2.0	4.0	2.0	2.0	1.0	1.0	1.0	5.0	4.0	1.0	3.0	1.0	1.0	1.0	3.0	2.0	-2.0	0.331667	0.443333	0.235139	0.262778	1.0	0.66	0.00	0.553333	0.00	1.0	0.000000	0.333333	0.0	0.0	0.0	0.000000	0.66	0.66	0.00	0.440000	0.0	0.0	0.0	0.000000	0.25	0.00	0.66	0.303333	0.000000	0.000000	0.666667	0.222222	0.33	0.5	0.415	0.415	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0
1	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070002.0	20211112.0	202111.0	202112.0	10.46	11.19	33.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	2.0	3.0	4.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	1.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	5.0	2.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	1.0	1.0	4.0	4.0	5.0	4.0	2.0	4.0	1.0	1.0	2.0	4.0	3.0	2.0	1.0	3.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	3.0	1.0	3.0	4.0	4.0	4.0	3.0	3.0	4.0	4.0	4.0	2.0	2.0	1.0	3.0	3.0	3.0	2.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	5.0	3.0	2.0	2.0	2.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	10.0	9.0	6.0	6.0	1.0	10.0	3.0	4.0	3.0	1.0	4.0	4.0	0.0	10.0	1.0	2.0	2.0	0.0	0.0	2.0	2.0	2.0	2.0	4.0	2.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	4.0	1.0	3.0	2.0	3.0	10.0	10.0	10.0	10.0	8.0	1.0	1.0	1.0	2.0	2.0	2.0	4.0	4.0	5.0	5.0	1.0	2.0	2.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	8.0	5.0	5.0	5.0	5.0	9.0	5.0	5.0	5.0	5.0	6.0	1.0	4.0	4.0	2.0	2.0	4.0	4.0	5.0	1.0	5.0	1.0	1.0	4.0	2.0	1.0	2.0	2.0	1.0	2.0	2.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	504009.0	504009.0	504009.0	4.0	3.0	4.0	4.0	4.0	4.0	1.0	1.0	3.0	2.0	1.0	5.0	1.0	1.0	4.0	1.0	1.0	1.0	10.0	10.0	10.0	10.0	10.0	10.0	10.0	10.0	5.0	8.0	6.0	7.0	3.0	1.0	1.0	1.0	2.0	3.0	4.0	2.0	1998.0	23.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	6.0	2.0	500.0	6.0	0.0	6.0	3.0	3.0	2.0	0.0	1.0	0.0	1.0	1.0	NaN	4.0	NaN	6.0	3.0	2.0	2.0	4.0	7.0	2.0	5.0	50000000.0	504005.0	5.0	1.0	5.0	5.0	5.0	5.0	1.0	1.0	1.0	5.0	5.0	5.0	1.0	5.0	3.0	3.0	3.0	2.0	5.0	5.0	1.0	1.0	5.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	5.0	1.0	1.0	0.0	NaN	NaN	2.0	1.0	-1.0	0.360556	0.111111	0.360556	0.387778	0.0	0.00	0.00	0.000000	0.00	0.0	0.666667	0.222222	1.0	1.0	1.0	1.000000	0.00	0.00	0.66	0.220000	0.0	1.0	1.0	0.666667	0.00	0.00	0.66	0.220000	0.444444	0.444444	0.777778	0.555556	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0
2	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504070003.0	20211112.0	202111.0	202112.0	11.28	12.09	41.0	2.0	504009.0	504019.0	504099.0	1.0	1.0	4.0	2.0	99.0	-8.84	29.49	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	2.0	1.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	3.0	2.0	3.0	3.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	1.0	1.0	3.0	4.0	2.0	2.0	3.0	3.0	3.0	2.0	1.0	1.0	2.0	2.0	1.0	4.0	4.0	4.0	4.0	4.0	2.0	1.0	2.0	3.0	4.0	1.0	1.0	3.0	3.0	4.0	1.0	2.0	4.0	1.0	1.0	1.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	4.0	4.0	4.0	1.0	2.0	1.0	9.0	3.0	3.0	2.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	1.0	1.0	10.0	2.0	3.0	2.0	2.0	3.0	4.0	4.0	10.0	4.0	0.0	2.0	2.0	2.0	0.0	2.0	2.0	0.0	2.0	2.0	3.0	4.0	4.0	2.0	3.0	3.0	1.0	1.0	1.0	2.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	1.0	1.0	2.0	1.0	4.0	2.0	3.0	8.0	8.0	7.0	5.0	2.0	7.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1.0	2.0	1.0	1.0	4.0	7.0	2.0	2.0	2.0	1.0	1.0	1.0	4.0	7.0	2.0	1.0	1.0	5.0	3.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	4.0	3.0	5.0	5.0	5.0	5.0	5.0	5.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	1.0	4.0	2.0	3.0	3.0	1.0	2.0	3.0	3.0	1.0	3.0	4.0	1.0	1.0	3.0	2.0	2.0	8.0	10.0	7.0	10.0	10.0	5.0	10.0	6.0	8.0	10.0	10.0	6.0	6.0	2.0	2.0	1.0	2.0	4.0	4.0	4.0	1.0	1986.0	35.0	3.0	2.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	2.0	170.0	3.0	1.0	6.0	3.0	4.0	2.0	6.0	3.0	6.0	3.0	7.0	NaN	9.0	9.0	9.0	3.0	1.0	2.0	2.0	7.0	2.0	5.0	50000000.0	504005.0	5.0	4.0	1.0	4.0	4.0	1.0	5.0	5.0	2.0	4.0	4.0	2.0	2.0	3.0	2.0	3.0	2.0	1.0	5.0	5.0	3.0	2.0	4.0	5.0	1.0	3.0	5.0	4.0	1.0	4.0	4.0	4.0	2.0	7.0	NaN	NaN	3.0	2.0	0.0	0.291389	0.249444	0.304583	0.360000	0.0	0.33	0.00	0.110000	0.00	1.0	0.166667	0.388889	1.0	1.0	0.0	0.666667	0.00	0.00	0.00	0.000000	0.0	0.0	1.0	0.333333	0.50	0.00	0.66	0.386667	0.000000	0.333333	0.666667	0.333333	0.33	0.0	0.165	0.165	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	1.0	1.0
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1196	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071197.0	20211213.0	202111.0	202112.0	15.30	16.25	55.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	2.0	1.0	1.0	0.833333	31083.33333	2.0	2.0	2.0	4.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	1.0	1.0	4.0	2.0	2.0	1.0	1.0	2.0	4.0	1.0	3.0	1.0	3.0	2.0	3.0	7.0	6.0	6.0	3.0	3.0	3.0	2.0	3.0	3.0	2.0	4.0	4.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	3.0	3.0	4.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	5.0	1.0	1.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	10.0	10.0	5.0	5.0	8.0	2.0	10.0	4.0	4.0	4.0	4.0	4.0	4.0	2.0	3.0	3.0	2.0	0.0	2.0	0.0	2.0	2.0	2.0	2.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	1.0	2.0	1.0	1.0	3.0	2.0	3.0	3.0	4.0	10.0	10.0	10.0	10.0	3.0	10.0	6.0	1.0	1.0	1.0	1.0	3.0	2.0	7.0	7.0	2.0	1.0	1.0	5.0	8.0	4.0	4.0	4.0	4.0	4.0	8.0	8.0	6.0	6.0	6.0	4.0	6.0	6.0	4.0	4.0	6.0	4.0	6.0	4.0	4.0	4.0	4.0	3.0	5.0	5.0	5.0	1.0	1.0	1.0	1.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	1.0	1.0	1.0	1.0	1.0	5.0	1.0	2.0	4.0	4.0	4.0	1.0	10.0	5.0	5.0	10.0	5.0	10.0	10.0	5.0	7.0	5.0	5.0	5.0	4.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1999.0	22.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	1.0	170.0	6.0	0.0	3.0	2.0	NaN	NaN	2.0	1.0	2.0	1.0	6.0	NaN	0.0	NaN	5.0	2.0	2.0	2.0	3.0	6.0	2.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	0.0	NaN	NaN	3.0	2.0	1.0	0.776667	0.610000	0.518889	0.581111	1.0	0.00	0.33	0.443333	0.33	1.0	1.000000	0.776667	1.0	1.0	1.0	1.000000	0.66	1.00	1.00	0.886667	1.0	0.0	0.0	0.333333	0.50	0.66	0.66	0.606667	0.333333	0.777778	0.555556	0.555556	0.66	0.5	0.580	0.580	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	1.0	0.0
1197	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071198.0	20211213.0	202111.0	202112.0	16.02	17.10	68.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	4.0	2.0	4.0	2.0	1.0	1.0	2.0	2.0	1.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	1.0	3.0	4.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	4.0	2.0	2.0	4.0	4.0	1.0	3.0	3.0	1.0	2.0	3.0	6.0	10.0	3.0	4.0	4.0	2.0	2.0	4.0	1.0	1.0	1.0	1.0	1.0	2.0	3.0	3.0	1.0	1.0	3.0	2.0	4.0	2.0	2.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	4.0	3.0	3.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	4.0	8.0	5.0	5.0	9.0	1.0	7.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	9.0	1.0	2.0	0.0	2.0	0.0	0.0	0.0	2.0	2.0	4.0	2.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	1.0	1.0	2.0	4.0	3.0	2.0	2.0	1.0	1.0	1.0	2.0	2.0	1.0	1.0	4.0	1.0	3.0	1.0	4.0	5.0	5.0	5.0	7.0	7.0	5.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	1.0	1.0	2.0	2.0	6.0	9.0	6.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	5.0	9.0	5.0	5.0	5.0	5.0	7.0	4.0	3.0	3.0	4.0	3.0	5.0	1.0	5.0	5.0	5.0	5.0	5.0	5.0	3.0	3.0	3.0	3.0	2.0	2.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	4.0	3.0	1.0	1.0	4.0	4.0	1.0	1.0	4.0	3.0	1.0	5.0	2.0	2.0	4.0	3.0	2.0	1.0	10.0	8.0	4.0	10.0	4.0	4.0	4.0	4.0	10.0	4.0	5.0	5.0	4.0	1.0	1.0	1.0	1.0	1.0	1.0	2.0	1957.0	64.0	5.0	3.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	6.0	1.0	170.0	1.0	4.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	5.0	4.0	0.0	3.0	4.0	2.0	2.0	2.0	3.0	1.0	1.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	2.0	4.0	4.0	2.0	2.0	2.0	2.0	4.0	2.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	4.0	NaN	NaN	0.0	1.0	-2.0	0.388333	0.166667	0.270278	0.540556	0.0	0.00	0.00	0.000000	0.00	0.0	1.000000	0.333333	1.0	1.0	1.0	1.000000	0.00	0.33	0.33	0.220000	0.0	0.0	0.0	0.000000	0.25	0.66	1.00	0.636667	0.444444	0.444444	0.444444	0.444444	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0
1198	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071199.0	20211213.0	202111.0	202112.0	17.10	18.02	52.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	2.0	1.0	0.833333	31083.33333	1.0	3.0	2.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	1.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	1.0	2.0	1.0	2.0	1.0	1.0	2.0	1.0	1.0	3.0	3.0	2.0	3.0	2.0	1.0	2.0	1.0	2.0	1.0	4.0	2.0	2.0	4.0	4.0	2.0	1.0	1.0	1.0	2.0	2.0	9.0	6.0	3.0	4.0	4.0	4.0	4.0	4.0	1.0	2.0	2.0	4.0	3.0	4.0	4.0	4.0	2.0	2.0	2.0	2.0	3.0	2.0	2.0	4.0	4.0	4.0	2.0	2.0	4.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	2.0	3.0	3.0	3.0	5.0	3.0	3.0	2.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	7.0	7.0	4.0	4.0	4.0	1.0	8.0	3.0	2.0	3.0	2.0	2.0	3.0	1.0	7.0	4.0	2.0	2.0	0.0	2.0	0.0	2.0	2.0	0.0	3.0	3.0	3.0	4.0	4.0	3.0	4.0	2.0	2.0	1.0	1.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	1.0	1.0	2.0	1.0	4.0	1.0	2.0	6.0	6.0	4.0	4.0	5.0	7.0	9.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	1.0	1.0	2.0	2.0	7.0	7.0	7.0	4.0	6.0	7.0	7.0	4.0	4.0	4.0	4.0	4.0	4.0	4.0	6.0	4.0	4.0	4.0	4.0	6.0	1.0	2.0	2.0	4.0	3.0	5.0	2.0	4.0	2.0	5.0	2.0	2.0	3.0	3.0	2.0	2.0	2.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	2.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	2.0	2.0	1.0	5.0	1.0	2.0	4.0	3.0	2.0	1.0	9.0	6.0	4.0	9.0	4.0	9.0	9.0	6.0	9.0	6.0	8.0	3.0	4.0	2.0	1.0	1.0	1.0	1.0	1.0	2.0	1978.0	43.0	3.0	2.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	4.0	1.0	170.0	1.0	2.0	2.0	1.0	4.0	2.0	1.0	1.0	1.0	1.0	5.0	2.0	0.0	3.0	6.0	2.0	2.0	2.0	3.0	3.0	1.0	5.0	50000000.0	504005.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	4.0	4.0	4.0	2.0	2.0	4.0	4.0	2.0	4.0	4.0	4.0	2.0	4.0	4.0	2.0	2.0	0.0	NaN	NaN	2.0	2.0	-1.0	0.443333	0.221667	0.283194	0.483889	0.0	0.33	0.00	0.110000	0.00	0.0	1.000000	0.333333	1.0	1.0	1.0	1.000000	0.33	0.33	0.33	0.330000	0.0	0.0	0.0	0.000000	0.25	0.66	0.66	0.523333	0.666667	0.333333	0.333333	0.444444	0.33	0.0	0.165	0.165	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	0.0	1.0
1199	0.0	0.0	2021.0	504.0	MAR	600.0	MOR	504071200.0	20211213.0	202111.0	202112.0	17.40	18.13	34.0	2.0	504002.0	504012.0	504081.0	5.0	3.0	3.0	1.0	81.0	-2.73	34.94	170.0	ar	1.0	1.0	1.0	0.833333	31083.33333	1.0	1.0	2.0	4.0	1.0	1.0	1.0	2.0	1.0	1.0	2.0	2.0	1.0	1.0	2.0	2.0	2.0	1.0	2.0	2.0	2.0	1.0	2.0	1.0	2.0	2.0	1.0	3.0	1.0	3.0	3.0	1.0	4.0	2.0	1.0	1.0	3.0	3.0	1.0	1.0	1.0	1.0	1.0	2.0	3.0	1.0	2.0	2.0	1.0	7.0	8.0	7.0	4.0	2.0	4.0	4.0	4.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	2.0	3.0	1.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	3.0	2.0	2.0	2.0	3.0	2.0	3.0	3.0	3.0	3.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	5.0	2.0	3.0	3.0	1.0	2.0	2.0	0.0	1.0	0.0	1.0	1.0	1.0	1.0	2.0	0.0	10.0	10.0	8.0	1.0	1.0	2.0	8.0	3.0	2.0	3.0	3.0	3.0	4.0	3.0	4.0	3.0	2.0	2.0	1.0	2.0	1.0	2.0	1.0	2.0	2.0	3.0	2.0	1.0	2.0	2.0	1.0	1.0	1.0	1.0	2.0	2.0	3.0	1.0	2.0	1.0	2.0	3.0	4.0	1.0	2.0	1.0	1.0	2.0	1.0	3.0	1.0	2.0	10.0	10.0	7.0	7.0	3.0	5.0	10.0	1.0	1.0	1.0	1.0	1.0	1.0	6.0	6.0	2.0	1.0	2.0	9.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	7.0	7.0	1.0	1.0	1.0	1.0	8.0	1.0	1.0	1.0	1.0	8.0	2.0	4.0	2.0	2.0	3.0	1.0	5.0	1.0	1.0	1.0	1.0	1.0	1.0	3.0	2.0	2.0	2.0	1.0	3.0	3.0	3.0	1.0	2.0	3.0	3.0	3.0	3.0	4.0	4.0	4.0	3.0	4.0	1.0	1.0	3.0	3.0	4.0	3.0	3.0	3.0	1.0	4.0	3.0	2.0	4.0	2.0	1.0	7.0	8.0	9.0	10.0	8.0	1.0	9.0	7.0	8.0	9.0	9.0	7.0	5.0	2.0	2.0	1.0	2.0	2.0	3.0	3.0	1.0	1998.0	23.0	1.0	1.0	1.0	1.0	1.0	504.0	504.0	504.0	1.0	3.0	1.0	170.0	6.0	0.0	4.0	2.0	NaN	NaN	2.0	1.0	2.0	1.0	7.0	NaN	0.0	NaN	5.0	3.0	2.0	2.0	3.0	7.0	2.0	5.0	50000000.0	504005.0	2.0	2.0	2.0	4.0	4.0	2.0	5.0	2.0	2.0	4.0	5.0	2.0	2.0	5.0	2.0	2.0	4.0	2.0	5.0	5.0	3.0	2.0	4.0	4.0	2.0	2.0	2.0	2.0	2.0	4.0	4.0	2.0	2.0	4.0	2.0	2.0	1.0	1.0	1.0	0.331944	0.443889	0.311944	0.457222	0.5	0.33	0.00	0.276667	0.00	1.0	0.833333	0.611111	0.0	0.0	0.0	0.000000	0.00	0.66	0.66	0.440000	0.0	0.0	1.0	0.333333	0.75	0.00	0.66	0.470000	0.000000	0.666667	0.666667	0.444444	0.00	0.0	0.000	0.000	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0

	Feeling of happiness (LV_Q46)	Feeling of happiness (NV_Q46)	count	perFoH	mean_SFSH
0	Very happy	1.0	149	12.416667	7.677852
1	Quite happy	2.0	845	70.416667	6.431953
2	Not very happy	3.0	195	16.250000	4.492308
3	Not at all happy	4.0	11	0.916667	2.818182

	ID_list	N_REGION_ISO	Satisfaction with financial situation of household (NV_Q50)
0	MA01	MA-01 Tanger-Tetouan-Al Hoceima	6.508333
1	MA02	MA-02 L'Oriental	6.262500
2	MA03	MA-03 Fes-Meknes	6.766667
3	MA04	MA-04 Rabat-Sale-Kenitra	6.141176
4	MA05	MA-05 Beni Mellal-Khenifra	5.975000
5	MA06	MA-06 Casablanca-Settat	6.080769
6	MA07	MA-07 Marrakech-Safi	6.033333
7	MA08	MA-08 Draa-Tafilalet	6.340000
8	MA09	MA-09 Souss-Massa	6.110000
9	MA10	MA-10 Guelmim-Oued Noun	6.200000
10	MA11	MA-11 Laayoune-Sakia El Hamra (EH-partial)	6.850000
11	MA12	MA-12 Dakhla-Oued Ed-Dahab (EH)	5.400000

	ID_list	regions_name	N_REGION_ISO	mean_FoH	mean_SFSH	count
0	MA01	Tanger-Tetouan-Al	MA-01 Tanger-Tetouan-Al Hoceima	2.941667	6.508333	120
1	MA02	L'Oriental	MA-02 L'Oriental	3.137500	6.262500	80
2	MA03	Fes-Meknes	MA-03 Fes-Meknes	3.020000	6.766667	150
3	MA04	Rabat-Sale-Kenitra	MA-04 Rabat-Sale-Kenitra	2.911765	6.141176	170
4	MA05	Beni	MA-05 Beni Mellal-Khenifra	2.812500	5.975000	80
5	MA06	Casablanca-Settat	MA-06 Casablanca-Settat	2.934615	6.080769	260
6	MA07	Marrakech-Safi	MA-07 Marrakech-Safi	2.893333	6.033333	150
7	MA08	Draa-Tafilalet	MA-08 Draa-Tafilalet	2.980000	6.340000	50
8	MA09	Souss-Massa	MA-09 Souss-Massa	2.940000	6.110000	100
9	MA10	Guelmim-Oued	MA-10 Guelmim-Oued Noun	2.900000	6.200000	10
10	MA11	Laayoune-Sakia	MA-11 Laayoune-Sakia El Hamra (EH-partial)	2.800000	6.850000	20
11	MA12	Dakhla-Oued	MA-12 Dakhla-Oued Ed-Dahab (EH)	3.000000	5.400000	10